Target tracking often involves comparing target tracks across multiple sensors to determine if the different tracks appearing on each sensor represent the same object and to combine the tracks on the sensors to gain a better understanding of the true location and velocity of the target. Hereafter location and velocity of an object in flight may collectively be called the “state” or “state vector” of the object. This comparison is often complicated by gaps between sensor scan areas and/or data latency from one sensor report to the next.
Traditional target tracking methodologies may often involve mathematically propagating individual observations to generate probability distributions for subsequent object states, using process and sensor noise estimates to generate the distribution. Subsequent observations may then be compared to the probability distribution to determine the appropriate correlation. A ballistic propagation model may be assumed as a default for object state prediction because object maneuvering may be generally inherently unpredictable. Deviations from the prediction may be handled according to magnitude and consistency as random process noise, measurement noise, target maneuvering or object appearance or disappearance. Heuristics may then determine whether the deviant measurement is incorporated into an overall understanding of the relationships among all objects relevant to a combat situation (hereinafter called the “battlespace”) as a track continuation, a track initiation, or a spurious measurement.
The most precisely reported quantity from most sensors used for tracking is the time of the measurement. Following in order of decreasing precision are position and velocity. In traditional target tracking methodologies, two state measurements may be associated if, by propagating the state vector (position and velocity) of the first state measurement, the second state vector is encountered within a radius of uncertainty that is considered reasonable.
Unfortunately, the time between the two measurements may only appear during the most error-laden piece of the hypothesis test, namely the propagation of the first state measurement. Thus, the precision of the most accurate information available (i.e., time of flight) may be wholly wasted, and the most imprecise information (i.e., the velocity of the initial state measurement) may be relied upon to create the probability distribution against which the final state measurement is compared.
Thus, the primary source of uncertainty or error in these methods is the propagation of the initial observation. Additionally, these methods may be slow and may waste valuable time needed for intercepting hostile targets.